Nonlinearity in the dynamics of financial markets

A new set of methodologies extracts key nonlinearities in the dynamics of financial markets from data that would appear to be completely random with ordinary linear time series methods. The understanding acquired from this analysis forms a basis for modeling conflicting and competing motivations in market decisions. By standardizing the daily changes using the mean and standard deviation, it then becomes possible to compare the quantitative impact of very different variables such as price trend and valuation, and the nonlinear relationship between them. The analysis of a large data set of closing stock prices provides strong statistical evidence that relative daily price change is positively influenced by valuation, recent price trend, short term volatility, volume trend and the M2 money supply. However, there is a strong nonlinearity in the influence of the price trend, so that a significantly large recent uptrend has a negative influence on the subsequent day’s relative price change. The nonlinearity is the key to understanding the conflicting role of price trend, since a single large data set exhibits both underreaction and overreaction in different regimes of the independent variables. The role of long term volatility is not a clear-cut risk/return inverse relation. But rather there is an ambiguous and complicated relationship between volatility and return. There is limited support for resistance when prices near the quarterly high. Mixed effects regressions are used after standardizing the data by subtracting the mean and dividing by one standard deviation individually for each of the 119 closed-end funds. A valuation variable is constructed in terms of the recent history of net asset value.     

Point and interval forecasting for carbon price based on an improved analysis-forecast system

Effective analysis and forecasting of carbon prices, which is an essential endeavor for the carbon trading market, is still considered a difficult task because of the nonlinearity and nonstationarity inherent in carbon prices. Previous studies have failed at the analysis and interval prediction of carbon prices and are limited to point forecasts. Therefore, an improved carbon price analysis and forecasting system that consists of an analysis module and a forecasting module is established in this study; more importantly, the forecasting module includes point forecasting and interval forecasting as well. Aimed at investigating the characteristics of the carbon price series, a chaotic analysis based on the maximum Lyapunov exponent is performed, the determination of appropriate distribution functions based on our newly proposed hybrid optimization algorithm is conducted, and different distribution functions are effectively designed in the analysis module. Furthermore, in the point forecasting model, the phase space reconstruction technique is applied to reconstruct the sequences decomposed by variational mode decomposition due to the chaotic characteristics of the carbon price series, and the reconstructed sequences are considered as the optimal input–output variables of the forecasting model. Then, an adaptive neuro-fuzzy inference system model is trained by the newly proposed hybrid optimization algorithm, which is developed for the first time in the domain of carbon price point forecasting. Moreover, based on the results of point forecasting and the distribution function of the carbon price series determined by the analysis module, the interval forecasting results can be obtained and implemented to provide more reliable information for decision making. Empirical results based on the carbon price data of the European Union Emissions Trading System and Shenzhen of China demonstrate that the proposed system achieves better results than other benchmark models in point forecasting as well as interval forecasting.     

天然气期货和现货价格间的相关性研究

基于天然气期货价格与现货价格序列间具有强非线性特征,本文将GARCH模型和Copula函数思想进行结合,同时考虑了天然气期货和现货价格间的时变相关结构,构建了时变Copula(GARCH-Normal、GARCH-GED和GARCH-t)模型,利用美国纽约商品交易所(NYMEX)Henry Hub交易中心天然气期货价格和现货价格数据进行实证研究。实证结果表明:GARCH-GED模型能够准确地拟合天然气期货与现货价格时间序列;时变SJC-Copula函数能够更好的描述天然气期货价格与现货价格间的相关性;天然气期货与现货价格间的相关性不是对称的,上尾的相关性小于下尾相的相关性。     

沪深300仿真股指期货价格不对称跳跃波动的实证分析

本研究利用2006年10月30日至2009年3月13日期间的仿真的沪深300指数期货每日结算价,探讨了期货价格的不对称跳跃波动行为。在实证研究方法上,本文以Chan和Maheu的GARCH(1,1)-ARJI模型为基础并进行了扩展,以EGARCH(1,1)-CJI和EGARCH(1,1)-ARJI两种模型来刻画股指期货价格的不对称和跳跃波动行为。实证结果显示:(1)沪深300仿真股指期货价格存在不对称跳跃波动,而且跳跃强度不为一固定常数,异常信息所产生的跳跃强度是随着时间变动的。(2)经过似然比检验,结果显示EGARCH(1,1)-ARJI模型比EGARCH(1,1)-CJI模型具有更好的拟合能力。     

STOCHASTIC PRICE MODELS AND OPTIMAL TREE CUTTING: RESULTS FOR LOBLOLLY PINE

An empirical investigation of stumpage price models and optimal harvest policies is conducted for loblolly pine plantations in the southeastern United States. The stationarity of monthly and quarterly series of sawtimber prices is analyzed using a unit root test. The statistical evidence supports stationary autoregressive models for the monthly series and for the quarterly series of opening month prices. In contrast, the evidence supports a non-stationary random walk model for the quarterly series of average prices. This conflicting result is likely an artifact of price averaging. The properties of these series significantly affect the forms of optimal price-dependent harvest rules and expected returns. Further, the results have implications for conclusions about market efficiency and the performance of a fixed rotation age.     

The efficiency of financial futures markets: Tests of prediction accuracy

The prices of financial futures contracts can be interpreted as forecasts of the spot rates, which will apply at the final delivery date of that contract. Financial futures contracts have been traded daily since the early 1980s and provide a substantial bank of data to test the forecasting efficiency of such contracts. Tests are carried out to examine whether the interest rates implied by the futures price for eurodollar and short sterling contracts are cointegrated with the final settlement price over forecasting horizons of 1, 2 and 3 months. Similar analysis is carried out for the yen/dollar exchange rate futures contract. The paper then examines the forecasting performance of the three contracts over the forecasting horizons of 1, 2 and 3 months and in particular whether the forecasts implied by the futures contract provide better predictions than the naı̈ve no-change (i.e. random walk), a vector error correction model (VECM) or an ARIMA model.An examination of the relative efficiency of the markets for the three markets over the three time horizons is carried out and finally trading strategies are simulated to see whether excess profits can be achieved. In fact the results suggest that both profits and losses would be attracted.     

Estimating intermediate price transitions in online auctions

This paper discusses a statistical model regarding intermediate price transitions of online auctions. The objective was to characterize the stochastic process by which prices of online auctions evolve and to estimate conditional intermediate price transition probabilities given current price, elapsed auction time, number of competing auctions, and calendar time. Conditions to ensure monotone price transitions in the current price and number of competing auctions are discussed and empirically validated. In particular, we show that over discrete periods, the intermediate price transitions are increasing in the current price, decreasing in the number of ongoing auctions at a diminishing rate, and decreasing over time. These results provide managerial insight into the effect of how online auctions are released and overlap. The proposed model is based on the framework of generalized linear models using a zero‐inflated gamma distribution. Empirical analysis and parameter estimation is based on data from eBay auctions conducted by Dell. Copyright © 2011 John Wiley & Sons, Ltd.     

基于Box-Jenkins方法的青南高原降水量时间序列分析建模与预测

应用基于Box-Jenkins方法的时间序列分析技术,对青南高原的四个典型地区1961-2005年降水量序列进行ARMA建模分析:验证了四地区年降水量序列的时间序列特性,研究并选择了这些序列的最佳ARMA模型,本文也通过模型对未来降水量进行了预测.模型实证分析的结果表明:在青藏高原降水量时间序列分析建模与预测方面,Box-Jenkins方法及其模型是一种精度较高且切实有效的方法模型.     

Self-criticality and stochasticity of an S&P 500 index time series

In this paper, three time series representative of the daily high, low and closing prices of S&P 500 index time series, as from 1 December 1988 to 1 April 1998 are studied. The hypothesis advanced by Osborne that the stock market time series satisfy a log-normal distribution is rejected. The self-critical behavior of these time series is investigated. A fractional Brownian motion model for such time series is supported. Arguments are directed torwards a negation of a chaotic explanation of these time series.     

Heavy fuel oil analysis at Iraq's Bayji refinery

This case study develops a dynamic model to analyse Heavy Fuel Oil (HFO) production and distribution at Iraq's largest oil refinery. It studies the impact of instituting a more flexible export pricing model for HFO removal, which constrains production due to physical storage limitations on refined fuel production and revenue generation. The production model utilizes system dynamics concepts and incorporates six independent, uncorrelated inputs (market prices, power outages, HFO produced, HFO removed via the power plant, local and export trucking) to effectively simulate historical trends. An export pricing model was then added to optimize the production model. Various sensitivities regarding the Iraq HFO export market to price fluctuations, time lag for prices changes to take effect, and production increases resultant of HFO storage becoming a less significant constraint were explored. The analysis reveals significant increased revenue potential, ranging from $50 million USD to $1 billion USD annually, by implementing a pricing system that adjusts HFO export prices based on HFO storage levels with minimal downside risk.     

Improving prediction market forecasts by detecting and correcting possible over-reaction to price movements

We examine the impact of price trends on the accuracy of forecasts from prediction markets. In particular, we study an electronic betting exchange market and construct independent variables from market price (odds) time series from 6058 individual markets (a dataset consisting of over 8.4 million price points). Using a conditional logit model, we find that a systematic relationship exists between trends in odds and the accuracy of odds-implied event probabilities; the relationship is consistent with participants over-reacting to price movements. In particular, in different time segments of the market, increasing and decreasing odds lead, respectively, to under- and over-estimation of odds-implied probabilities. We develop a methodology to detect and correct the erroneous forecasts associated with these trends in odds in order to considerably improve the quality of forecasts generated in prediction markets.     

基于支持向量回归机的黄金价格短期预测

针对黄金价格时间序列的特点,首先结合马尔可夫决策思想对数据集进行相空间重构处理,然后利用支持向量机技术建立黄金价格走势的短期预测模型,最后对上海黄金交易所AU9999的预测结果表明所建模型可以有效地进行黄金价格的短期预测.     

Reverse bullwhip effect in pricing

Price variability is one of the major causes of the bullwhip effect. This paper analyzes the impact of procurement price variability in the upstream of a supply chain on the downstream retail prices. Procurement prices may fluctuate over time, for example, when the supply chain players deploy auction type procurement mechanisms, or if the prices are dictated in market exchanges. A game theory framework is used here to model a serial supply chain. Sequential price game scenarios are investigated to show that there is an increase in retail price variability and an amplified reverse bullwhip effect on prices (RBP) under certain demand conditions.     

Optimal electricity generation portfolios

It is common practice to base investment decisions on price projections which are gained from simulations using price processes. The choice of the underlying process is crucial for the simulation outcome. For power plants the core question is the existence of stable long-term cointegration relations. Therefore we investigate the impacts of different ways to model price movements in a portfolio selection model for the German electricity market. Three different approaches of modelling fuel prices are compared: initially, all prices are modelled as correlated random walks. Thereafter the coal price is modelled as random walk. The gas price follows the coal price through a mean-reversion process. Lastly, all prices are modelled as mean reversion processes with correlated residuals. The prices of electricity base and peak futures are simulated using historical correlations with gas and coal prices. Yearly base and peak prices are transformed into an estimated price duration curve followed by the steps power plant dispatch, operational margin and net present value calculation and finally the portfolio selection. The analysis shows that the chosen price process assumptions have significant impacts on the resulting portfolio structure and the weights of individual technologies.     

基于相空间重构技术的原油价格的时序混沌判据和混沌特征量

通过相空间重构技术,对Brent和WTI原油价格增长率的时间序列分别进行相空间重构,将若干固定时间延迟点上的数据作为新维处理,形成相点,应用Wolf方法得出了最大的Lyapunov指数,从而给出了系统混沌存在的证据;利用关联函数求出了关联维度和Kolmogorov熵,从而给出了对系统的混沌程度的估计和对Brent和WTI原油价格进行有效性预测的时间尺度.     

TEMPORAL AGGREGATION AND TESTING FOR TIMBER PRICE BEHAVIOR

ABSTRACT. Different harvest timing models make different assumptions about timber price behavior. Those seeking to optimize harvest timing are thus first faced with a decision regarding which assumption of price behavior is appropriate for their market, particularly regarding the presence of a unit root in the timber price time series. Unfortunately for landowners and investors, the literature provides conflicting guidance on this subject. One source for the ambiguous results of unit root tests of timber prices may involve data problems. We used Monte Carlo simulations to show that aggregating observations below their observed rate resulted in similar power reductions and empirical size distortions across three classes of unit root tests. Moving‐average error structures can also affect power and sizes of tests on period‐averaged data. Such error structures can also be created by the kind of temporal averaging common in reported timber prices. If we take timber prices at their face value and therefore ignore these sampling error and temporal aggregation complications, we find that unit root tests on southern timber prices support a unit root in 158 out of 208 product‐deflation combinations tested, random walks in 38 of the series found to be nonsta‐tionary, and stationarity in none. However, if we recognize temporal aggregation errors, unit root tests more commonly favor stationarity, especially for pulpwood stumpage. Because price trends for sawtimber and pulpwood products may behave differently even in the same region, stochastic harvest timing models must be developed that allow their multiple products to follow different price paths.     

LP models for pricing diffuse nitrate discharge permits

Nitrate discharges from diffuse agricultural sources significantly contribute to groundwater and surface water pollution. Tradable permit programs have been proposed as a means of controlling nitrate emissions efficiently, but trading is complicated by the dispersed and delayed effects of the diffuse pollution. Hence, markets in nitrate discharge permits should be carefully designed to account for the underlying spatial and temporal interactions. Nitrate permit markets can be designed similar to the modern electricity markets which use LPs to find the equilibrium prices because the two trading problems have close analogy. In this paper, we propose alternative LP models to find efficient permit prices for year-ahead markets. The model structure varies depending on the catchment hydro-geology and long-term goals of the community. We show how the market price structures are driven by the constraint structure under different environmental conditions. We discuss the physical and economic conditions required to assure consistent prices, the modeling of essential and optional constraints in an LP, and the problem of balancing resource allocation over time among delayed-response discharge units. We then extend the LP model to balance resource allocation over time and to improve the market performance.     

Causality analysis of futures sugar prices in Zhengzhou based on graphical models for multivariate time series

This paper presents a method of constructing a mixed graph which can be used to analyze the causality for multivariate time series. We construct a partial correlation graph at first which is an undirected graph. For every undirected edge in the partial correlation graph, the measures of linear feedback between two time series can help us decide its direction, then we obtain the mixed graph. Using this method, we construct a mixed graph for futures sugar prices in Zhengzhou (ZF), spot sugar prices in Zhengzhou (ZS) and futures sugar prices in New York (NF). The result shows that there is a bi-directional causality between ZF and ZS, an unidirectional causality from NF to ZF, but no causality between NF and ZS.     

外汇市场、股票市场与房地产市场的风险传染研究——基于三元VAR-BEKK-GARCH模型实证分析

本文将人民币汇率、房价和股价三者纳入一个统一的分析框架中,从水平变动和波动风险两个方面考虑时变异方差和变量间的风险传递效应,使用“二次汇改”后的2010年6月到2017年12月的月度数据,采用三元GARCH和BEKK时序模型研究人民币汇率、房价和股价之间的动态影响关系及其波动风险互动机制。研究发现,三个市场相互之间具有明显的影响,特别是价格波动的风险传染上,房地产市场与股票之间、股票市场与汇率市场之间或长期或短期都存在风险的传递效应。具体而言,市场在均值溢出方面,人民币升值会促进房价和股价的上涨;但房价与股价之间的价格影响关系并不明显。在波动溢出方面,房价和股价之间的波动溢出效应明显,同时存在ARCH和GARCH型波动效应,而股价对汇率的波动影响也同时存在ARCH和GARCH型波动效应,但汇率对股价仅有GARCH型波动效应。     

Time-Varying Parameters Prediction

This paper develops a method of adaptive modeling that may be applied to forecast non-stationary time series. The starting point are time-varying coefficients models introduced in statistics, econometrics and engineering. The basic step of modeling is represented by the implementation of adaptive recursive estimators for tracking parameters. This is achieved by unifying basic algorithms—such as recursive least squares (RLS) and extended Kalman filter (EKF)—into a general scheme and next by selecting its coefficients with the minimization of the sum of squared prediction errors. This defines a non-linear estimation problem that may be analyzed in the context of the conditional least squares (CLS) theory. A numerical application on the IBM stock price series of Box-Jenkins illustrates the method and shows its good forecasting ability.